As　the　most　comprehensive　document　types　for　the　recording　and　display　of　real-world　information　regarding　construction　projects,　3D　realistic　models　are　capable　of　recording　and　displaying　simultaneously　textures　and　geometric　shapes　in　the　same　3D　scene.　However,　at　present,　the　documentation　for　much　of　construction　infrastructure　faces　significant　challenges.　Based　on　TLS,　GNSS/IMU,　mature　photogrammetry,　a　UAV　platform,　computer　vision　technologies,　and　AI　algorithms,　this　study　proposes　a　workflow　for　3D　modeling　of　complex　structures　with　multiple-source　data.　A　deep　learning　LoFTR　network　was　used　first　for　image　matching,　which　can　improve　matching　accuracy.　Then,　a　NeuralRecon　network　was　employed　to　generate　a　3D　point　cloud　with　global　consistency.　GNSS　information　was　used　to　reduce　search　space　in　image　matching　and　produce　an　accurate　transformation　matrix　between　the　image　scene　and　the　global　reference　system.　In　addition,　to　enhance　the　effectiveness　and　efficiency　of　the　co-registration　of　the　two-source　point　clouds,　an　RPM-net　was　used.　The　proposed　workflow　processed　the　3D　laser　point　cloud　and　UAV　low-altitude　multi-view　image　data　to　generate　a　complete,　accurate,　high-resolution,　and　detailed　3D　model.　Experimental　validation　on　a　real　high　formwork　project　was　carried　out,　and　the　result　indicates　that　the　generated　3D　model　has　satisfactory　accuracy　with　a　registration　error　value　of　5　cm.　Model　comparison　between　the　TLS,　image-based,　data　fusion　1　(using　the　common　method),　and　data　fusion　2　(using　the　proposed　method)　models　were　conducted　in　terms　of　completeness,　geometrical　accuracy,　texture　appearance,　and　appeal　to　professionals.　The　results　denote　that　the　generated　3D　model　has　similar　accuracy　to　the　TLS　model　yet　also　provides　a　complete　model　with　a　photorealistic　appearance　that　most　professionals　chose　as　their　favorite.